Multi-class classification is a challenging problem in pattern recognition. Clustering-based Classification (CC) is one of the most effective classification methods that first divides data into several clusters, each cluster then being described by a One-Class Classifier (OCC). Scalability and accuracy are two key advantages of this clustering-enhanced approach. In continuation of this strategy, in this paper, we further propose Spectral Clustering-based Classification (SCC). In contrast to many other clustering algorithms, Spectral Clustering (SC) aims to put the more mutually interconnected data points in one cluster, hence producing output clusters with smoother borders. A simpler border is easier to be described by an OCC, leading to higher accuracy. Application to seven UCI data sets of various nature and size confirms this improved performance in terms of higher accuracy, while keeping scalability property.